【摘要】 用密度泛函方法在6-31G(d)基组上优化了38种聚丙烯酸酯类的结构单元,得到了其单元的量子化学参数,探讨了这些参数与聚丙烯酸酯类玻璃化温度(Tg)的关系.计算表明,影响聚丙烯酸酯类Tg的主要因素有结构单元的侧链长度、侧链的分支数、最高占据轨道能级、极化率、偶极矩、等体积热容和热力学能等参数.用模式识别方法(偏最小二乘法)讨论了这些参数与Tg的定性关系,两类Tg大小不同的聚合物基本分布在不同区域,用逐步回归和人工神经网络方法建立了这些参数与Tg的定量关系,2种方法的预测结果与实验值的相关系数分别为0.9753、0.9985,标准偏差分别为18.42、4.25,预报结果与实验值基本一致.更多还原

【Abstract】 The mechanism and affecting factors of the glass transition for polymers have been analyzed. The structural units of thirty-eight polyacrylates have been optimized and their quantum chemical descriptors have been obtained by DFT/6-31G(d) method. The calculated results indicate that the length of side chain, number of side chains, polarizability, dipole moment, EHOMO, heat capacity at constant volume, and thermal energy are the main factors affecting glass transition temperature(Tg). The regularity of Tg for polyacrylates are discussed by the pattern recognition method (PLS) with quantum chemical descriptors as features. The two classes of polymers with different Tg distribute in different regions. The quantitative relationship have been studied between these descriptors and Tg by stepwise regression and BP-ANN(back propagation artificial neural network) methods. The correlation coefficients between the predicted and experimental Tg for the two methods are 0.9753 and 0.9985, and the standard deviations are 18.42 and results 4.25, respectively.更多还原